基于核相关滤波器的SAR车辆目标检测与识别

潘卓; 王宾辉; 高鑫; 王岩飞

doi:10.3724/SP.J.1146.2008.00221

基于核相关滤波器的SAR车辆目标检测与识别

doi: 10.3724/SP.J.1146.2008.00221

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出版历程
- 收稿日期: 2008-02-27
- 修回日期: 2008-10-27
- 刊出日期: 2009-05-19

Kernel Correlation Filter for Vehicle Detection and Recognition in SAR Images

摘要

摘要: 针对合成孔径雷达(Synthetic Aperture Radar, SAR)目标检测与识别方法对目标方位角敏感的问题，该文基于相关滤波器理论与核特征分析方法，提出一种对SAR目标方位角具有较强鲁棒性的核相关滤波器。该滤波器使用特征向量降低了对训练图像的依赖性，利用目标在非线性空间的高维特征提高了识别能力，并利用核函数解决了高维矢量的内积计算问题。MSTAR实测SAR图像数据的对比实验结果表明，该文所提出的滤波器在低虚警概率下，能够保持较高的检测概率，并且对目标方位角失真具有较强的容忍性，不需要存储目标模板和估计目标方位角，就能够实现高准确率的目标检测与识别。
- SAR图像;目标检测;目标识别;相关滤波器;核函数
Abstract: SAR target detection and recognition is sensitive to targets azimuth. To solve the problem, based on correlation theory and kernel feature analysis, a kernel correlation filter which is strongly robust to targets azimuth distortion is proposed. The novel filter exploits eigenvectors to reduce the dependence of the training set and extends linear combination of eigenvectors nonlinearly to improve the classification. Moreover, to keep the computation tractable in high dimensional space, the kernel function is employed. Comparative tests using MSTAR database demonstrate the kernel correlation filter performs high detection probability with low false alarm probability and implements target detection and recognition accurately without templates and target poses estimation.

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